Physical Sciences and Mathematics Commons^™

Body Mass Index Has No Impact On Complications And Mortality For Patients With Stage Iv Pancreatic Ductal Adenocarcinoma, Ren Bryant, Hannah Darnell, Megan Hall, Kelsey N. Karnik, Kristen J. Mcquerry, Ruben R. Plentz

Biostatistics Faculty Publications

Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the leading causes of United States (USA) cancer death. Overweight and obesity developing into a growing global medical and socio-economic problem, affecting approximately 42% of adults in the USA population. The aim of our analysis was to evaluate the influence of overweight and obesity on complications and clinical outcome in patients with stage IV PDAC.

Methods: We retrospectively reviewed electronic health records of patients diagnosed with stage IV PDAC (n=162) who followed with the University of Kentucky from January 2017–October 2024. Comparisons were based on the body mass index (BMI): low BMI …

Flood Risk Prediction System For Irish River Networks, Fernando Adalberto Naatz Heringer, Hugo Rodrigues De Freitas

ICT

Flooding costs the Irish economy more than 300 million euros annually; however, the hydrometric and meteorological data needed to predict dangerous river rises is already collected and freely available. This project applies machine learning to a decade of hourly observations from OPW Station 09001 on the River Liffey at Leixlip and Met Eireann Casement Aerodrome to build a flood risk prediction system with a 72-hour forecast horizon.

A two-phase modelling pipeline was built following the CRISP-DM framework. Phase 1 compared LSTM and GRU recurrent neural networks on the task of forecasting the river level in metres 72 hours ahead. LSTM …

Machine Learning System For Cereal Yield Prediction In South America., Victor Gabriel Oliveira, Kelvin Henrique Ferreira Dumas

ICT

This project developed a machine learning solution to predict cereal yield in South America using FAOSTAT agricultural data. The objective was to estimate yield in kg/ha for rice, maize, and wheat based on variables such as country, crop type, year, harvested area, producer price, pesticide use, and nutrient indicators.

The project followed the CRISP-DM methodology, covering business understanding, data understanding, data preparation, modelling, evaluation, and deployment. Several regression models were tested, including Random Forest, Gradient Boosting, AdaBoost, XGBoost, KNN, and SVR.

The models were evaluated using MAE, MSE, RMSE, and R² score. The results showed that tree-based ensemble models performed …

A Comparative Analysis Of Supervised Machine Learning Techniques For Predicting It Incident Resolution Time, Ammad Hussain, Hussnain Yaqoob

ICT

IT support teams are facing the persistent challenge in estimating how long an incident will take to resolve. The lack of reliable predictions, planning decisions around staffing, escalation and user communication are largely guesswork. This project set out to address that gap by building a machine learning solution capable of predicting incident resolution time from details available at the point a ticket is logged.

Using the UCI IT Incident Event Log dataset, which contains 141,712 records from a real service management system, the project followed the CRISP-DM framework across six phases from business understanding through to deployment. After thorough data …

A Comprehensive Study Of Sarima And Xgboost Models For Short-Term Traffic Volume Forecasting At A Melbourne Intersection (Victoria), Australia, Douglas Vinicius Dierings, Felipe Fontanive Marques

ICT

Traffic congestion is an increasing challenge in modern cities, impacting transportation efficiency and urban sustainability. This project focuses on short-term traffic volume forecasting, using 15-minute Sydney Coordinated Adaptive Traffic System (SCATS) data collected from the State of Victoria, Australia. The study compares two forecasting approaches: SARIMA, a statistical time-series model, and XGBoost, a decision-tree machine-learning model. Historical traffic data from 2022 to 2024 was analysed to identify traffic patterns, seasonal trends, and peak traffic periods. The models were evaluated for their effectiveness in handling urban traffic behaviour. The results of this study aim to support smarter traffic management, infrastructure planning, …

Identifying New Programme Development Viability, Grant Goodwin

ICT

Higher Education Institutions (HEIs) face the challenge of developing programmes that respond to changing labour market needs while providing graduates with relevant employment opportunities and ensuring institutional financial viability. Rapid technological and economic changes make it increasingly important for HEIs to use reliable data to identify emerging skills and workforce demands. This project explores the use of machine learning and nationally collected employment data to support evidence-based decision-making in higher education programme development. By analysing patterns and relationships within labour market data, machine learning models can help identify areas of growing demand and provide insights into the potential alignment between …

Seasonal Dublin Airport Passenger Arrivals Forecast For The Economy Sector Of Dublin Airport, Roberto Tavares De Oliveira Neto

ICT

Dublin Airport plays a significant role in Ireland’s tourism and commercial sectors, with passenger demand directly influencing retail, hospitality, car rental and other airport-related businesses. Accurate forecasting of passenger numbers can support these sectors in planning marketing activities, managing stock levels and making evidence-based operational decisions. This project develops a monthly forecasting framework for passenger arrivals at Dublin Airport using data from the Central Statistics Office (CSO). Building on previous exploratory data analysis, the study evaluates five time series forecasting approaches: ARIMA, SARIMA, Holt-Winters Triple Exponential Smoothing, TBATS, and an automated model comparison pipeline using PyCaret. The models are assessed …

Machine Learning For Early Obesity Risk Prediction, Giuseppe Cutugno

ICT

Obesity is a major global public health concern, with significant implications for individual health, healthcare systems, and wider economic outcomes. Early identification of obesity risk can support preventive strategies and encourage healthier lifestyle choices before health conditions become more severe. This project develops a machine learning framework for estimating obesity levels using data relating to individuals’ physical characteristics, eating habits, lifestyle behaviours, and demographic factors. Following the CRISP-DM framework, the project examines the structure and limitations of the dataset, including its mixture of objective measurements, survey-based variables, and synthetically generated observations. Two modelling approaches are considered, one including BMI as …

Understanding Host Behaviour And Property Review Characteristics Using New York City Airbnb Dataset, Anna Szkwara

ICT

This project applies strategic thinking and the CRISP-DM methodology to analyse Airbnb listings in New York City and identify patterns associated with host performance and market vulnerability. Airbnb has become a major global accommodation platform, creating opportunities for individual hosts while also increasing competition within the short-term rental market. The project focuses on whether data-driven analysis can identify factors that may influence host success and provide useful insights for hosts with less experience or expertise than larger hospitality organisations. The dataset contains 85 variables, many of which require careful evaluation due to redundancy, high cardinality, sensitivity, or limited relevance to …

Predicting Product Returns In Online Retail, Mihaela Cristina Stefanache

ICT

Product returns are a significant challenge for online retailers, contributing to additional operational costs and affecting inventory management and profitability. This project develops and evaluates three machine learning models to predict product returns for a UK online retailer, building on the data understanding and preparation completed in the previous CA2 capstone project. Following the CRISP-DM framework, the study extends the analysis through enhanced exploratory data analysis, feature engineering, model development, hyperparameter optimisation, cross-validation, and model interpretability. Logistic Regression, a Tuned Random Forest, and XGBoost are evaluated, with the Tuned Random Forest achieving the strongest overall performance, obtaining an F1 score …

Characterizing Cyber Intrusions In Critical Infrastructure Networks Using Discrete-Event Simulation, Lawrence M. Dilworth

Dissertations, Master's Theses and Master's Reports

Over the past two decades, cybersecurity compliance frameworks such as the North American Electric Reliability Corporation Critical Infrastructure Protection (CIP) have introduced prescriptive measures for protecting power system networks, emphasizing restricted access, segmentation, and minimizing routable exposure. While effective for baseline cyber hygiene, these approaches do not capture system-level risks or adversarial propagation across interconnected infrastructure. In contrast, Cyber-Informed Engineering (CIE), advanced by Idaho National Laboratory, embeds security in system design by considering threat vectors and physical constraints.

Despite CIP guidance, many deployments rely on IP-routable, bidirectional communication that enables handshaking, allowing adversaries to infer reachable targets. This work presents …

Changing Snow Hydrology In The Great Lakes And Resilience Through Adaptive Management, Aradea R. Hakim

Dissertations, Master's Theses and Master's Reports

A number of studies have found that global snow cover is generally decreasing, but a closer examination at regional scales shows greater complexity. In the Great Lakes basin, contrasting trends have been identified. While snow cover is generally decreasing basinwide, leeward (downwind) regions have experienced increases in snowfall and snow cover. Changes in snow dynamics are also associated with shifts in runoff timing and volume due to earlier snowmelt and changing precipitation patterns. Streamflow analyses at gauges across the Great Lakes basin indicate trends toward earlier peak runoff and longer runoff periods, although these patterns vary among watersheds and lake …

Bayesian Analysis Of Nominal Outcomes With Missing Values Using Multinomial And Multivariate Multinomial Probit Models, Suwash Silwal

Dissertations, Master's Theses and Master's Reports

Nominal outcomes frequently arise in health sciences, transportation, economics, market research, and related fields. These data often contain missing values, while longitudinal and panel studies generate multiple correlated nominal responses. Bayesian estimation of multinomial probit (MNP) and multivariate multinomial probit (MMNP) models provides a flexible framework for analyzing such data but remains computationally challenging due to high-dimensional likelihood integration, restrictive covariance identification constraints, and poor mixing of Markov chain Monte Carlo (MCMC) algorithms, particularly in the presence of missing data. This dissertation develops parameter-expanded data augmentation (PX-DA) methods for MNP and MMNP models with missing nominal outcomes by incorporating parameter …

Slip Kinematics And Structural Analysis Of The Keweenaw Fault System From Lake Linden To Hancock, Michigan, Katherine M. Langfield

Dissertations, Master's Theses and Master's Reports

The Keweenaw fault is a crustal-scale fault spatially associated with Mesoproterozoic rocks of the Midcontinent Rift System. Along the fault, older Portage Lake Volcanics have been thrust southeastward over younger Jacobsville sandstone. Ideas on the fault’s origin, from oldest to most recent, include that it is: (1) a reverse fault, (2) a normal fault formed during midcontinent rifting that was reactivated and inverted to a reverse fault during the Grenville orogeny, and (3) a detached thrust fault system initiated during the Grenville orogeny.

This thesis is based on a U.S. Geological Survey EdMap grant to remap a portion of the …

Real Time Waste Classification Using Deep Learning: Comparing Mobilenetv2 And Resnet 18 With Transfer Learning And Fine Tuning, Abdul Moaiz

ICT

Waste contamination is a major problem all over the world. The Environmental Protection Agency reports that over two thirds of waste found in general household and commercial bins could have been placed in the recycling or organic waste bins instead in Ireland, with food waste and plastics being the most common misplaced items. This project presents a deep learning solution to classify nine categories of waste from camera images in real time with the goal of helping users sort waste correctly at the source. Following the CRISP DM framework, two convolutional neural network architectures were trained and compared, MobilenetV2 and …

Bitcoin Prediction System Usind Machine Learning Techniques, Carolina Azevedo De Castro

ICT

This project investigated the use of machine learning techniques to predict short-term Bitcoin price direction using historical market data obtained from Yahoo Finance. Following the CRISP-DM methodology, the dataset was analysed, prepared, and transformed through feature engineering techniques including moving averages, volatility indicators, return measures and price position metrics. Multiple classification algorithms were evaluated, including Bayesian Classification, K-Nearest Neighbour, Decision Tree, Random Forest, Logistic Regression, Support Vector Machine and XGBoost. Several optimisation strategies were also tested, including feature selection, hyperparameter tuning, feature scaling and class weight balancing. Results showed that predicting short-term Bitcoin price movements remains challenging, with most models …

Predictive Maintenance For Manufacturing Equipment, Paloma De Andrade Batista

ICT

Unplanned equipment downtime is a significant challenge in manufacturing, resulting in substantial productivity losses and operational costs. Predictive maintenance, enabled by machine learning and big data analytics, offers an opportunity to identify potential equipment failures before they occur and improve maintenance efficiency. This report extends a previous capstone project that applied Random Forest and Logistic Regression to the AI4I 2020 Predictive Maintenance Dataset. The current study expands the analysis by incorporating XGBoost, systematic hyperparameter optimisation, cross-validation, and SHAP-based model interpretability. In addition, SWOT and PESTLE analyses, alongside a legal and ethical assessment, examine the broader strategic and responsible implementation of …

Predicting Loneliness Among Older Adults In Ireland Using Supervised Machine Learning, Ariadne Chaves Miranda

ICT

Over the last two decades, technology has grown exponentially and has facilitated communication that helps individuals remain connected. However, this has also contributed to social isolation and lack of physical interaction. One consequence of this phenomenon is loneliness, which is understood as an unpleasant subjective state of discrepancy between the desired amount of companionship or emotional support and what is available in the person’s environment (Prohaska and Burholt, 2020). In the European context, Ireland has emerged as the loneliest country in Europe with 20% of its population that have reported feeling lonely most or all the time (Schnepf et al., …

Feasibility Of Upcycling Spent Lithium-Ion Battery To Carbon Dioxide Capture Adsorbent, Chimezie Frank Onwudinjo

Master’s Theses

This study investigates the feasibility of repurposing spent lithium-ion battery (SLIB) to lithium orthosilicate (Li₄SiO₄), a high temperature carbon dioxide sorbent. Two synthesis pathways including conventional acid-leaching method (Scenario 1) and a pyrolysis-based route (Scenario 2) were explored. Additionally, techno-economic analysis (TEA) and lifecycle assessment (LCA) of the two processes were also performed. Different analytical characterization techniques were performed to understand the material properties of Li₄SiO₄ including surface area, crystallinity, morphology and thermal stability. CO₂ capture performance of the synthesized Li₄SiO₄ was tested in a thermogravimetric analyzer (TGA) using …

Beyond The Lace Index: Benchmarking Machine Learning Architectures And Explaining 30-Day Hospital Readmission Risk With Shap Analysis, Carl E. Hughes Iii

Williams Honors College, Honors Research Projects

Unplanned 30-day hospital readmission remains a fundamental challenge in US healthcare, associated with increased risk to patient recovery and representing an estimated $52.4 billion in annual expenses (Beauvais et al., 2022). While the rigorously validated LACE index serves as the clinical standard for readmission modeling, its linear structure and four explanatory variables lack the complexity to capture the high-dimensional and interactive nature of patient risk. This study utilizes an admission granularity level cohort of the MIMIC-IV database to develop and compare machine learning architectures against the baseline LACE index. Due to the imbalanced prevalence of readmission, the penalized logistic regression, …

Rate Response Of Curlyleaf Pondweed And Native Plants To Flumioxazin In Minnesota Lakes, Pearl J. Jensen

All Graduate Theses, Dissertations, and Other Capstone Projects

Curlyleaf pondweed (Potamogeton crispus L.) is an invasive aquatic plant present across the United States that causes significant recreational issues, outcompetes native plant species, and depletes water quality. Before senescing in mid-summer, curlyleaf pondweed forms a large number of turions that can lay dormant in the sediment for several years. Typical herbicide management of this species occurs in early spring to target the plant before turion production occurs and while native plants are dormant. Flumioxazin has shown potential in curlyleaf pondweed management in small scale studies, but its effects have not been assessed in operational management. In this study, treatments …

Designing Ai Systems To Support A Productive-Failure-Based Learning: Insights From Adult Learners On Ai Applications And Ai System Design Principles, Jinhee Kim, Xi Lin, Seongryeong Yu, Rita Detrick

STEMPS Faculty Publications

Emerging capabilities of generative artificial intelligence (GenAI) offer significant potential to support productive failure (PF)-based learning, which engages adult learners (ALs) in exploring problems before instruction and learning from their initial attempts. However, the effective use of AI to support multifaceted areas of PF-based learning, including problem generation, exploration, consolidation, and knowledge assembly, is limited. Furthermore, AI design principles to support PF-based learning remain under-researched. This study, therefore, aims to investigate ALs’ perceptions of AI applications in enhancing PF-based learning and to explore the essential design principles of AI systems for PF-based learning. To achieve these aims, the study conducted …

Modeling Housing Prices: Which Features Matter Most?, Alex Ruvolo

Williams Honors College, Honors Research Projects

This paper attempts to find the biggest factors and traits that influence the cost of housing. This will include the lot size, type of street, utilities, neighborhood, year built, heating, electrical, yard size, number of different rooms, age, condition, and others. I will attempt to answer the question of whether the prices of houses have changed within the last 5 to 10 years, and obviously this is an easy question to answer. However, the bigger question beyond this is are the main factors affecting housing prices all important in explaining this relationship? Is one factor more important than the rest …

Catalan And Hyper-Catalan Numbers: Combinatorial Applications To Polynomial Equations, Leilani Natale

Williams Honors College, Honors Research Projects

In this paper, we study Catalan numbers and their generalization, hyper-Catalan numbers, and explore how these sequences arise naturally in the context of solving polynomial equations using infinite power series. We begin by introducing the Catalan numbers through their combinatorial interpretation as triangulations of convex polygons. Using this geometric definition, we derive a relation whose recursive structure leads to a quadratic functional equation. Interpreting this relation as a formal power series equation allows us to express solutions to quadratic equations as infinite power series whose coefficients are given by the Catalan numbers. This framework is then extended by allowing polygon …

Comprehensive Analytical Investigation Of Tetrahydro-4h-Chromene And Dihydropyridine Derivatives, Ryan J. Burk

Chemistry & Biochemistry Dissertations

Novel calcium channel blockers (CCBs) with dihydropyridine (DHP) or tetrahydro-4H-chromene moieties are often chiral, so effective enantiomer separation methods are needed for continued drug development. The purpose of this dissertation is to outline a comprehensive method development model for the chiral separations of DHPs and tetrahydro-4H-chromene derivatives. The scope of this dissertation is to address analytical challenges during initial synthesis, chiral separations, and data analysis during post-processing for this class of chiral compounds. The first section focuses on the method development strategies for separations of DHPs in the sub/supercritical fluid (SFC) and normal phase modes with the 2-hydroxypropyl-β-cyclodextrin stationary phase. …

Novel Methods For Environmental Fluoride Measurement, Cable Warren

Chemistry & Biochemistry Dissertations

Fluoride analysis has been an important focus of analytical analysis for many years and will continue to be so going forward. Today, fluorinated compounds, specifically per/polyfluoroalkyl substances (PFAS), better known as “forever chemicals” have captured the moment and are the subject of vast research and regulation. Under this context, I have researched new methods of fluoride separation and concentration in complex media as well as a novel method of PFAS destruction and total organic fluorine analysis for screening of PFAS in aqueous samples. Through research and work on micro-scale detection methods, an understanding of the state of the art and …

Labor Savings From Mechanical Rice Transplanting In Bangladesh, India, Nepal, And The Philippines: A Systematic Review And Meta-Analysis, Rodrigo S. David

ASEAN Journal on Science and Technology for Development

Rice production in South and Southeast Asia faces serious challenges, including acute labor shortages and rising wages, which threaten regional food security. Mechanical rice transplanting offers a promising solution; however, evidence on labor savings, especially region-specific data, remains inconsistent. This systematic review and meta-analysis aimed to quantify labor savings from mechanical versus manual transplanting across Bangladesh, India, Nepal, and the Philippines.

Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic search was conducted across four major scientific databases (2000–2026) using Boolean search terms for mechanical transplanting and labor outcomes. Of 284 initial records, 52 met eligibility …

Climate And Seasonal Influences On Indo-Pacific Sea Level: Insights From 30 Years Of Satellite And Tide Gauge Data, Karlina Triana

ASEAN Journal on Science and Technology for Development

We analyze Indo-Pacific sea-level variability using monthly satellite altimetry (1993–2025) validated against records from 16 tide-gauge stations. Cross-comparisons show strong agreement, especially in the western Pacific, confirming the reliability of altimetry for regional assessments. Seasonal SLA variability is largest in the Bay of Bengal and South China Sea and reflects monsoonal forcing, whereas interannual fluctuations in the eastern Indian and western Pacific oceans are dominated by ENSO and modulated by PDO. Harmonic decomposition isolates annual and semi-annual cycles, and an EOF/PCA framework identifies the leading modes: EOF1 (30.8%) captures basin-scale interannual variability and EOF2 (20.9%) reflects the seasonal cycle. Spectral …

Pullulan Production From Lignocellulosic Plant Biomass Or Starch-Containing Processing Coproduct Hydrolysates, Thomas P. West

Faculty Publications

The complex polysaccharide pullulan is characterized as a glucose-containing biopolymer that is both water-soluble and neutral in polarity. A variety of commercial applications exist for pullulan, including its utilization as a flocculant, a blood plasma substitute, a food additive, a dielectric material, an adhesive, or a packaging film. The fungus Aureobasidium pullulans has used several hydrolysates derived from plant biomass or starch-containing processing coproducts to support polysaccharide production. These include various plant biomass or processing coproduct streams such as lignocellulosic-containing peat, prairie grass, stalks, hulls, straw, shells, and pods or starch-containing coproducts from the processing of corn, rice, jackfruit seeds, …

A Simple Statistical Tool To Correct The Daily Temperature Effect On Dielectric Soil Matric Potential Sensor Readings, Sebastián Bravo Peña, Meindert Commelin, Ole O. Wendroth

Plant and Soil Sciences Faculty Publications

High-resolution time series recorded by soil water dielectric sensors are often affected by other processes. Dielectric soil matric potential sensors, such as the TEROS 21, exhibit temperature sensitivity, resulting in deviations of readings from the true value due to soil temperature fluctuations. However, methods for correcting this effect remain limited. The objective of this study was to create a straightforward, scale-based statistical approach to mitigate the influence of daily temperature oscillations on matric potential dielectric readings. The temperature sensitivity correction function (TSCF) identifies, quantifies, and smooths diurnal fluctuations caused by soil temperature dynamics. We provide a detailed description of the …